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"""Tokenization classes for GeoV.""" |
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from pathlib import Path |
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from typing import List, Optional, Tuple |
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import sentencepiece as spm |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from transformers.utils import SPIECE_UNDERLINE, logging |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": { |
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"GeoV/GeoV-9b": "https://huggingface.co/GeoV/GeoV-9b/resolve/main/spiece.model", |
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} |
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} |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"GeoV-9b": 2048, |
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} |
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class GeoVTokenizer(PreTrainedTokenizer): |
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""" |
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Construct an GeoV tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). |
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to |
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this superclass for more information regarding those methods. |
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Args: |
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vocab_file (`str`): |
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that |
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contains the vocabulary necessary to instantiate a tokenizer. |
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bos_token (`str`, *optional*, defaults to `"<s>"`): |
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The beginning of sequence token that was used during pretraining. |
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eos_token (`str`, *optional*, defaults to `"</s>"`): |
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The end of sequence token. |
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unk_token (`str`, *optional*, defaults to `"<unk>"`): |
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this |
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token instead. |
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new_line_token_id (`int`, *optional*, defaults to `65_499`): |
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The token id of new line character. |
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Attributes: |
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sp_model (`SentencePieceProcessor`): |
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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vocab_file, |
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bos_token="<s>", |
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eos_token="</s>", |
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unk_token="<unk>", |
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new_line_token_id=65_499, |
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**kwargs, |
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) -> None: |
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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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unk_token=unk_token, |
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new_line_token_id=new_line_token_id, |
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**kwargs, |
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) |
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self.vocab_file = vocab_file |
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self.new_line_token_id = new_line_token_id |
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self.sp_model = spm.SentencePieceProcessor() |
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self.sp_model.Load(vocab_file) |
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@property |
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def vocab_size(self): |
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return len(self.sp_model) |
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def get_vocab(self): |
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def __getstate__(self): |
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state = self.__dict__.copy() |
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state["sp_model"] = None |
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return state |
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def __setstate__(self, d): |
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self.__dict__ = d |
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self.sp_model = spm.SentencePieceProcessor() |
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self.sp_model.Load(self.vocab_file) |
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def _tokenize(self, text: str) -> List[str]: |
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"""Tokenize a string.""" |
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ret = [] |
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split_text = text.splitlines() |
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for l in split_text: |
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rl = self.sp_model.encode(l, out_type=str) |
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ret.extend(rl) |
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ret.append("\n") |
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ret = ret[:-1] |
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return ret |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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if token == "\n": |
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return self.new_line_token_id |
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return self.sp_model.PieceToId(token) |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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if index == self.new_line_token_id: |
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return "\n" |
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return self.sp_model.IdToPiece(index) |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (strings for sub-words) in a single string.""" |
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out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() |
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return out_string |
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def _decode( |
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self, |
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token_ids: List[int], |
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skip_special_tokens: bool = False, |
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clean_up_tokenization_spaces: bool = True, |
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spaces_between_special_tokens: bool = True, |
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**kwargs, |
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) -> str: |
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filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) |
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if skip_special_tokens: |
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filtered_tokens = [t for t in filtered_tokens if t not in self.all_special_ids] |
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text = self.convert_tokens_to_string(filtered_tokens) |
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if clean_up_tokenization_spaces: |
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clean_text = self.clean_up_tokenization(text) |
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return clean_text |
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else: |
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return text |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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save_directory = Path(save_directory) |
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if not save_directory.is_dir(): |
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raise ValueError(f"Vocabulary path ({save_directory}) should be a directory") |
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vocab_fn = VOCAB_FILES_NAMES["vocab_file"] |
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filename_prefix = f"{filename_prefix}-" if filename_prefix else "" |
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vocab_file = save_directory / f"{filename_prefix}{vocab_fn}" |
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with open(str(vocab_file), "wb") as fi: |
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content_spiece_model = self.sp_model.serialized_model_proto() |
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fi.write(content_spiece_model) |
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return (str(vocab_file),) |
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